{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0c481241",
   "metadata": {},
   "source": [
    "# Случайная величина"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da41fdb0",
   "metadata": {},
   "source": [
    "<strong>Случайная величина</strong> - функция $f : Ω → E$, $(Ω, F), (E, C)$ - измеримые пространства",
    "\n",
    "1. Дискретные СВ - счетное количество значений (например подбрасывание кости)",
    "\n",
    "Распределения: биномиальное, геометрическое, Пуассона\n",
    "\n",
    "2. Непрерывные СВ - принимают <i>все</i> значения из промежутка (например температура в комнате)",
    "\n",
    "Абсолютно непрерывные распределения: нормальное, равномерное, экспоненциальное",
    "\n",
    "\n",
    "<strong>Измеримость</strong> - $ⱯA ∈ C : f^{-1}(A) ∈ F$ или же означает что, для любой случайной величины можно определить её вероятность\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b597e6a",
   "metadata": {},
   "source": [
    "<strong>Случайные величины независимы</strong> если известное значение одной из них не дает информации о другой.",
    "\n",
    "$ξ, η$ - независимые значения случайной величины, если $ⱯB₁ ∈ ℬ(ℛ^{n}), B₂ ∈ ℬ(ℛ^{k}) : P(ξ ∈ B₁, η ∈ B₂) = P(ξ ∈ B₁) * P(η ∈ B₂)$",
    "\n",
    "\n",
    "<strong>Непрерывная функция от случайной величины</strong> возвращает случайную величину, ",
    "sin(ξ) - случайная величина.",
    "\n",
    "<strong>Характеристическая функция</strong> случайной величины один из способов задания распределения.\n",
    "Это преобразование Фурье распределения случайной величины."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1eab489b",
   "metadata": {},
   "source": [
    "<strong>Функция распределения случайной величины ξ</strong> - функция принимающая значение x и возвращающая вероятность того, что $F$(x) = P(ξ < x)\n",
    "\n",
    "(Ω, F, P) - вероятностное пространство, на нем задана случайная величина X, с распределением ℙ<sup>X</sup>, ",
    "функция распределения величины X - это функция $F$<sub>X</sub>: ℝ → [0, 1]",
    "\n",
    "\n",
    "$F$<sub>X</sub>(x) = ℙ(X < x) ≡ ℙ<sup>X</sup>((-∞, x))",
    "\n",
    "\n",
    "Находится по формуле: $F(x)=\\int_{-\\infty}^xP(w)dw$",
    "\n",
    "\n",
    "Свойcтва:", 
    "\n",
    "1. $F$(-∞) = 0", 
    "\n",
    "2. $F$(+∞) = 1", 
    "\n",
    "3. Если x₁ < x₂, то $F$(x₁) ≤  $F$(x₂)", 
    "\n",
    "4. F - неубывающая", 
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "468f27b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import scipy.stats as sps\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "507b7a65",
   "metadata": {},
   "source": [
    "<strong>График функции нормального распределения</strong>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "058fb22a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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Z2UpPT290nYqKiuMCR0BA/V8PxphG13E4HIqIiGhwAwBv8v6GAq3YUSxHoF2/Z8p34Iw0e57irKwsTZw4UcOGDdOIESM0a9YslZeXa9KkSZKkCRMmKDk5WdOnT5ckXXXVVZo5c6YGDx6stLQ0bdu2TQ899JCuuuoqTygBAF9SVevSo+/Vn7T6q4t7qkt0mMUVAb6t2WFk7NixOnjwoKZOnaqCggINGjRIS5Ys8ZzUmp+f36An5MEHH5TNZtODDz6ovXv3qnPnzrrqqqv06KOPttxWAEAb+tsnO7S3pFJJkSG6/eKeVpcD+DybOdGxEi/idDoVGRmp0tJSDtkAsNTekkpd+uQyVdW69fS4wbpqIBOcASfS1O9vrk0DAM0wffFmVdW6NaJ7jH48INHqcgC/QBgBgCZas6tY767fL7tNmnbVOcy0CrQQwggANIExRo8trp/c8RfDUtQvKdLiigD/QRgBgCb4YGOh1uw6rNCgAN39o7OsLgfwK4QRADiFWpdbjy+p7xW55cLuio8IsbgiwL8QRgDgFF5fla+dReXq1CFYtzKUF2hxhBEAOImy6jrN+vBbSdJdGb0V7mj29EwAToEwAgAn8fwn23WovEY9Yjvov0d0tbocwC8RRgDgBAqdVXrhs52SpHsv76ugAD4ygdbAbxYAnMCsD79VZa1LQ7tFK7NfvNXlAH6LMAIAjcgrKtcbq3dLkqaM7ssEZ0ArIowAQCNmfbhVLrfRJX06a1hqjNXlAH6NMAIAP7Cl4Ije+WqfJOm3l/WxuBrA/xFGAOAHnlq6VcZIo/snqH8y074DrY0wAgDf8/WeUi3ZWCCbTcpi2negTRBGAOB7/vKfLZKknw5KVu/4jhZXA7QPhBEAOOrLvGJ9svWgAu023ZnR2+pygHaDMAIAkowxeuKD+l6Ra4elqFunDhZXBLQfhBEAkLR8+yGt2lms4EC7/ufSXlaXA7QrhBEA7Z4xRn89ejG860Z0VWJkqMUVAe0LYQRAu7diR7FW5RUrOMCu2y7uaXU5QLtDGAHQ7v01e6skaezwFCVEhlhcDdD+EEYAtGsrdxzSih3FCgqw6fZL6BUBrEAYAdCu/e9H9eeKXDssRUlRnCsCWIEwAqDdWp1XrC+2HVKg3aZf0ysCWIYwAqDd+mt2fa/Iz4d2UZfoMIurAdovwgiAdmlt/mF99m2RAuw2TR7FvCKAlQgjANqlp4/2ivxscLJSYugVAaxEGAHQ7mzcV6qPtxyU3SZ6RQAvQBgB0O48t2y7JOnKAUlKjeUaNIDVCCMA2pWdReVa/PV+SWIEDeAlCCMA2pW/fbJdbiP9V984nZ0YYXU5AEQYAdCOFJRW6V9r90iSJo+iVwTwFoQRAO3GC5/tUK3LaET3GA3tFmN1OQCOIowAaBcOl9dowcp8SYygAbwNYQRAuzBveZ4qa13qlxShi3rHWl0OgO8hjADwe2XVdXppeZ6k+l4Rm81mbUEAGiCMAPB7C7/crdLKWvWI7aDMfglWlwPgBwgjAPxarcutuZ/vlCTdclEPBdjpFQG8DWEEgF9b/PV+7S2pVGx4sH46ONnqcgA0gjACwG8ZY/TCZzskSRPTUxUSFGBxRQAaQxgB4Ldyth/Shr1OhQTZ9cvzulldDoATIIwA8FvPH+0V+cWwFEV3CLa4GgAnQhgB4Je2FBzRsi0HZbdJN13Q3epyAJwEYQSAXzp2rsjl/RPUrVMHi6sBcDKEEQB+p9BZpXdy90qSbrmwh8XVADgVwggAvzN/eZ5qXUbDU6M1uGu01eUAOAXCCAC/UlZdp3+s2CVJuvWinhZXA6ApCCMA/MrCL3frSFWdesR20KV946wuB0ATEEYA+I267039fvOFPWRn6nfAJxBGAPiN945O/d6pQ7B+NoSp3wFfQRgB4BcaTP0+kqnfAV9CGAHgF1btLGbqd8BHEUYA+IW5X9SfK/KzIV0Uw9TvgE8hjADwefmHKvSfTYWSpEkjU60tBkCzEUYA+LyXcvJkjHTRWZ3VO76j1eUAaCbCCACfdqSqVgu/3C1JuvH8VGuLAXBaTiuMzJ49W6mpqQoJCVFaWppWrVp10uVLSko0efJkJSYmyuFw6KyzztLixYtPq2AA+L431+xRWXWdenbuoIt6d7a6HACnIbC5KyxcuFBZWVmaM2eO0tLSNGvWLGVmZmrLli2Kizt+tsOamhr96Ec/UlxcnN58800lJydr165dioqKaon6AbRjLrfR/OV5kqRJ53dnkjPARzU7jMycOVO33HKLJk2aJEmaM2eO3nvvPc2dO1f333//ccvPnTtXxcXFWr58uYKCgiRJqampZ1Y1AEj66JsD2nWoQpGhQUxyBviwZh2mqamp0Zo1a5SRkfHdC9jtysjIUE5OTqPr/Pvf/1Z6eromT56s+Ph49e/fX4899phcLtcJ36e6ulpOp7PBDQB+6NjU7+NGdFVYcLP/tgLgJZoVRoqKiuRyuRQfH9+gPT4+XgUFBY2us2PHDr355ptyuVxavHixHnroIT355JP605/+dML3mT59uiIjIz23lJSU5pQJoB3YtM+pnB2HFGC3aUI6k5wBvqzVR9O43W7FxcXp+eef19ChQzV27Fg98MADmjNnzgnXmTJlikpLSz233bt3t3aZAHzMvKOTnI3un6CkqFCLqwFwJprVrxkbG6uAgAAVFhY2aC8sLFRCQkKj6yQmJiooKEgBAd9dJ+Lss89WQUGBampqFBx8/EyJDodDDoejOaUBaEeKyqr1Tu4+SdKNF3S3uBoAZ6pZPSPBwcEaOnSosrOzPW1ut1vZ2dlKT09vdJ3zzz9f27Ztk9vt9rRt3bpViYmJjQYRADiVV1fkq8bl1qCUKA3pGm11OQDOULMP02RlZemFF17QSy+9pM2bN+v2229XeXm5Z3TNhAkTNGXKFM/yt99+u4qLi3XnnXdq69ateu+99/TYY49p8uTJLbcVANqN6jqXXlmxSxK9IoC/aPbp52PHjtXBgwc1depUFRQUaNCgQVqyZInnpNb8/HzZ7d9lnJSUFH3wwQe6++67NWDAACUnJ+vOO+/Ufffd13JbAaDdePer/Soqq1ZCRIhG92/88DAA32IzxhirizgVp9OpyMhIlZaWKiIiwupyAFjEGKMfP/25Nu5z6t7L++jXl/SyuiQAJ9HU72+uTQPAZ6zZdVgb9znlCLRr3PCuVpcDoIUQRgD4jGNTv/9kUJKiO3ACPOAvCCMAfEKhs0pLNtRPrjhxZKq1xQBoUYQRAD7h1RW7VOc2Gp4arX5JkVaXA6AFEUYAeL3qOpcWrMqXRK8I4I8IIwC83uKv96uorEYJESHK7MdwXsDfEEYAeL35y+snORuf1lVBAXxsAf6G32oAXi13d4m+2l2i4AC7xqUxnBfwR4QRAF7t5aPDeX88IFGx4VxAE/BHhBEAXquorFrvrt8viRNXAX9GGAHgtV5b+d3VeQemRFldDoBWQhgB4JVqXW79Y2X9ias30CsC+DXCCACv9MHGAhU6qxUbHqzR5zKcF/BnhBEAXumloyeuXjeiqxyBAdYWA6BVEUYAeJ2N+0r1Zd5hBdptGn9eN6vLAdDKCCMAvM7LRyc5u7x/guIjQiyuBkBrI4wA8CqHy2u0KHevJE5cBdoLwggAr7Jw9W5V17nVLylCQ7tFW10OgDZAGAHgNVxuo1dy6g/RTByZKpvNZnFFANoCYQSA1/hwc6H2llQqOixIVw9MsrocAG2EMALAaxwbzvvfI7oqJIjhvEB7QRgB4BW+LTyi5dsPyW6TfslwXqBdIYwA8Aov5eRJki47J0HJUaHWFgOgTRFGAFjOWVWrt9bWD+fl6rxA+0MYAWC5f67eo4oal/rEd9R5PWKsLgdAGyOMALCU2230ytFDNBNGdmM4L9AOEUYAWOqTrQeVd6hCHUMC9dPByVaXA8AChBEAlpp/dDjv2GEpCgsOtLYYAJYgjACwzI6DZfpk60HZbNKE9FSrywFgEcIIAMu8sqJ+6vf/6hOnrp3CLK4GgFUIIwAsUV5dpzdX75HEcF6gvSOMALDEW2v36Eh1nXp07qALesVaXQ4ACxFGALQ5Y4xeOnZ13vRU2e0M5wXaM8IIgDb3xbZD2nagTOGOQF0ztIvV5QCwGGEEQJs7Npz350O7KNzBcF6gvSOMAGhTu4srlP1NoSTp+nSuzguAMAKgjb2yYpeMkS7sHauencOtLgeAFyCMAGgzlTUuLfxytyTpBobzAjiKMAKgzSzK3avSylp1jQnTJX3irC4HgJcgjABoE8YYvXT0xNUJ6d0UwHBeAEcRRgC0iVU7i/VNwRGFBgXo2mEpVpcDwIsQRgC0iZdy8iRJPx2SrMjQIGuLAeBVCCMAWt2+kkp9sLF+OO9Ers4L4AcIIwBa3asrd8nlNkrv0Ul9EjpaXQ4AL0MYAdCqqmpdem1V/XDeiSOZ5AzA8QgjAFrVu+v3q7i8RkmRIco4O97qcgB4IcIIgFbz/eG8v0zvpsAAPnIAHI9PBgCtZm1+ib7eW6rgQLv+e3hXq8sB4KUIIwBazbFekZ8MTFJMh2BriwHgtQgjAFrFAWeVFn+9X5I0kevQADgJwgiAVrFgVb7q3EbDukWrf3Kk1eUA8GKEEQAtrqbOrVdX5kuiVwTAqRFGALS49zfs18Ej1YqPcOjy/glWlwPAyxFGALS4eV/kSZLGp3VTEMN5AZwCnxIAWtTa/MPK3V2i4EC7rktjOC+AUyOMAGhRcz/fKUkaMyhJseEOi6sB4AtOK4zMnj1bqampCgkJUVpamlatWtWk9V5//XXZbDaNGTPmdN4WgJfbV1Kp9zcUSJImnd/d4moA+Ipmh5GFCxcqKytL06ZN09q1azVw4EBlZmbqwIEDJ10vLy9Pv/3tb3XhhReedrEAvNvLOfVX5x3Zs5POToywuhwAPqLZYWTmzJm65ZZbNGnSJJ1zzjmaM2eOwsLCNHfu3BOu43K5NH78eD388MPq0aPHGRUMwDtV1NTptVX1w3npFQHQHM0KIzU1NVqzZo0yMjK+ewG7XRkZGcrJyTnheo888oji4uJ00003Nel9qqur5XQ6G9wAeLe31u5VaWWtunUK03/1jbO6HAA+pFlhpKioSC6XS/HxDS8DHh8fr4KCgkbX+fzzz/Xiiy/qhRdeaPL7TJ8+XZGRkZ5bSkpKc8oE0MbcbqN5X9SfuHrDyFQF2G0WVwTAl7TqaJojR47o+uuv1wsvvKDY2NgmrzdlyhSVlpZ6brt3727FKgGcqU+/PajtB8vV0RGoa4fxxwOA5glszsKxsbEKCAhQYWFhg/bCwkIlJBw/y+L27duVl5enq666ytPmdrvr3zgwUFu2bFHPnj2PW8/hcMjhYEgg4CvmHp3k7BfDUxTuaNbHCgA0r2ckODhYQ4cOVXZ2tqfN7XYrOztb6enpxy3ft29fff3118rNzfXcrr76ao0aNUq5ubkcfgH8wLeFR/Tp1oOy2+oP0QBAczX7T5isrCxNnDhRw4YN04gRIzRr1iyVl5dr0qRJkqQJEyYoOTlZ06dPV0hIiPr3799g/aioKEk6rh2Ab5q3PE+S9KNz4pUSE2ZtMQB8UrPDyNixY3Xw4EFNnTpVBQUFGjRokJYsWeI5qTU/P192OxO7Au3B4fIavbV2jyTpRobzAjhNNmOMsbqIU3E6nYqMjFRpaakiIphICfAWzy7bpj8v2aJ+SRF6944LZLMxigbAd5r6/U0XBoDTUuty6+XluyTV94oQRACcLsIIgNOy+Ov9KnBWKTbcoR8PTLS6HAA+jDACoNmMMXrhsx2SpAnp3eQIDLC4IgC+jDACoNlyth/Shr1OhQTZdf153awuB4CPI4wAaLbnj/aK/GJYiqI7BFtcDQBfRxgB0CxbCo5o2Zb6Sc5uuoDhvADOHGEEQLMcO1fk8v4J6tapg8XVAPAHhBEATVborNI7uXslSbdc2MPiagD4C8IIgCab90Weal1Gw1OjNbhrtNXlAPAThBEATVJWXadXV9ZPcnbrRcdfbRsAThdhBECTvL4qX0eq6tSjcwdd2jfO6nIA+BHCCIBTqnW5Ne+LPEn154rY7Uz9DqDlEEYAnNLir/drb0mlYsOD9dPByVaXA8DPEEYAnJQxRs9/emzq91SFBDH1O4CWRRgBcFI52w9p4z6mfgfQeggjAE6Kqd8BtDbCCIAT2rzfydTvAFodYQTACT23bLskaXT/RKZ+B9BqCCMAGrXrULneXb9PknT7JUxyBqD1EEYANGrOJzvkNtIlfTqrf3Kk1eUA8GOEEQDHKXRW6V9r9kiSfn1JL4urAeDvCCMAjvP3z3aoxuXWsG7RGtE9xupyAPg5wgiABkoqavTqynxJ0uRR9IoAaH2EEQANvLR8lypqXDo7MUKX9OlsdTkA2gHCCACP8uo6zVu+U5L060t6ymbjgngAWh9hBIDHa6vyVVJRq9ROYbri3ESrywHQThBGAEiSqmpd+tvRC+L96uKeCrDTKwKgbRBGAEiSXl+Vr4NHqpUcFaprhnSxuhwA7QhhBICqal167pP6qd9vv6SnggP5aADQdvjEAaB/rt6tQme1EiNDdO0wekUAtC3CCNDOVde59Oyy73pFHIEBFlcEoL0hjADt3Jtr9mh/aZXiIxz6xbAUq8sB0A4RRoB2rKbOrWc/ru8Vue3ingoJolcEQNsjjADt2Ftr92hvSaU6d3Ro3IiuVpcDoJ0ijADtVK3LrdnLtkmSfnVRD3pFAFiGMAK0U2+u2aPdxZWKDQ/W+LRuVpcDoB0jjADtUFWtS/+b/a0k6fZLeik0mF4RANYhjADt0IKV+dpfWqXEyBCNT+NcEQDWIowA7UxFTZ2ePXquyB3/1ZtzRQBYjjACtDPzl+epqKxG3TqFMdsqAK9AGAHakdLKWv3tk/or896V0VtBAXwEALAen0RAO/LiZztUWlmr3nHhunpgstXlAIAkwgjQbhwqq9aLn++UJN1z2VkKsNssrggA6hFGgHZizifbVV7jUv/kCGX2S7C6HADwIIwA7cDu4gq9tHyXJOm3l/WRzUavCADvQRgB2oGZS7eqxuXWyJ6ddPFZna0uBwAaIIwAfm7D3lK9vW6vJOn3V5xNrwgAr0MYAfyYMUaPLd4sSRozKEn9kyMtrggAjkcYAfzYJ1sPavn2QwoOsOuey/pYXQ4ANIowAvgpl9toxvvfSJImjuymlJgwiysCgMYRRgA/9a+1e/RNwRFFhARq8qheVpcDACdEGAH8UGWNSzP/s1WS9Jv/6qWosGCLKwKAEyOMAH7ob59uV4GzSslRoZqQnmp1OQBwUoQRwM/sLanUnE+2S5KmXNFXIUEBFlcEACdHGAH8zGOLN6uq1q207jG68txEq8sBgFMijAB+ZMWOQ3pv/X7ZbdK0q/oxwRkAn3BaYWT27NlKTU1VSEiI0tLStGrVqhMu+8ILL+jCCy9UdHS0oqOjlZGRcdLlAZwel9vo4f/bJEkaN6KrzkmKsLgiAGiaZoeRhQsXKisrS9OmTdPatWs1cOBAZWZm6sCBA40uv2zZMo0bN04ff/yxcnJylJKSossuu0x79+494+IBfOe1VfnavN+piJBAJjgD4FNsxhjTnBXS0tI0fPhwPfPMM5Ikt9utlJQU3XHHHbr//vtPub7L5VJ0dLSeeeYZTZgwoUnv6XQ6FRkZqdLSUkVE8Nce8EOlFbW65C8f63BFrf5w1Tm64fzuVpcEAE3+/m5Wz0hNTY3WrFmjjIyM717AbldGRoZycnKa9BoVFRWqra1VTEzMCZeprq6W0+lscANwYjOXbtHhilqdFR+uX57XzepyAKBZmhVGioqK5HK5FB8f36A9Pj5eBQUFTXqN++67T0lJSQ0CzQ9Nnz5dkZGRnltKSkpzygTalfV7SvTKil2S6k9aDQzgvHQAvqVNP7VmzJih119/XW+//bZCQkJOuNyUKVNUWlrque3evbsNqwR8R53Lrd+//bXcRvrJoCSd3yvW6pIAoNkCm7NwbGysAgICVFhY2KC9sLBQCQkJJ133L3/5i2bMmKEPP/xQAwYMOOmyDodDDoejOaUB7dLLObu0YW/9SasPXnmO1eUAwGlpVs9IcHCwhg4dquzsbE+b2+1Wdna20tPTT7jen//8Z/3xj3/UkiVLNGzYsNOvFoDH/tJKPfmfLZKk+0efrc4dCfAAfFOzekYkKSsrSxMnTtSwYcM0YsQIzZo1S+Xl5Zo0aZIkacKECUpOTtb06dMlSY8//rimTp2qBQsWKDU11XNuSXh4uMLDw1twU4D25Q//3qjyGpeGdovWfw/nvCoAvqvZYWTs2LE6ePCgpk6dqoKCAg0aNEhLlizxnNSan58vu/27DpfnnntONTU1+vnPf97gdaZNm6Y//OEPZ1Y90E59uKlQH2wsVKDdpkd/2l92OzOtAvBdzZ5nxArMMwJ8p7y6Tpc99an2llTqtot76v7Rfa0uCQAa1SrzjACw3uNLvtHekkp1iQ7VnZf2trocADhjhBHAhyzfXqSXc+rnFJnxswEKDQ6wuCIAOHOEEcBHlFfX6d4310uSrkvrqgt6M6cIAP9AGAF8xPT3N2vP4UolR4Xq91ecbXU5ANBiCCOAD1i+rUj/WJEvSXri5wMU7mj2QDgA8FqEEcDLlVXX6XdHD89cf143jWTKdwB+hjACeLlH39usvSWVSokJZRgvAL9EGAG82JINBXptVf3hmcevGaAOHJ4B4IcII4CX2ldSqfv+VX945lcX99DInhyeAeCfCCOAF3K5je56PVellbUa2CVS9/yoj9UlAUCrIYwAXuiZj7ZpVV6xwh2B+t9xgxUcyK8qAP/FJxzgZVbnFeuv2VslSX8a01/dOnWwuCIAaF2EEcCLlFbU6s7Xc+U20s8GJ2vM4GSrSwKAVkcYAbyE221018J12ltSqdROYXpkTH+rSwKANkEYAbzErA+36uMtB+UItOuZ64YwyyqAdoMwAniBpZsK9b8fbZMkzbjmXPVPjrS4IgBoO4QRwGLbD5Ypa2GuJOmGkan66eAu1hYEAG2MMAJYqKy6Tre9skZHqus0IjVGD1zJ1XgBtD+EEcAibrfRb9/4St8eKFN8hEPPjB+soAB+JQG0P3zyARZ5fMk3WrKxQMEBdj33y6GK6xhidUkAYAnCCGCBV1bs0t8+3SFJeuLaARrSNdriigDAOoQRoI199E2hpr2zQZL028vO0k8GMbEZgPaNMAK0oQ17S/WbBevkNtIvhnXR5FG9rC4JACxHGAHayJ7DFbpx/peqqHHpgl6xevSn58pms1ldFgBYjjACtIFCZ5XG/32lDhypVp/4jnr2l0MYOQMAR/FpCLSyQ2XVGv/3ldp1qEIpMaGaf+NwRYQEWV0WAHgNwgjQikoravXLF1dp24EyJUaGaMHN5ykxMtTqsgDAqxBGgFZypKpWE+at0ub9TsWGO/TqzWlKiQmzuiwA8DqEEaAVHKmq1Y3zv9RXu0sUFRakV29OU4/O4VaXBQBeiWuUAy2suLxGE+eu0td7S9XREahXbkxTn4SOVpcFAF6LMAK0oILSKv3yxZXadqBMMR2C9fKNI9Q/OdLqsgDAqxFGgBaSf6hC419cod3FlUqICNE/bk5TrzgOzQDAqRBGgBaweb9TE+eu0oEj1UrtFKZXbuJkVQBoKsIIcIY+3FSoO19fp/Ial/rEd9QrN4/gCrwA0AyEEeA0GWP0wmc7NP39b2SMNLJnJz03fqgiw5jQDACagzACnIaaOrceXPS13li9R5J0XVpXPXx1P6Z4B4DTQBgBmqnQWaU7FqzTqrxi2W3SQz8+RzeMTOWidwBwmggjQDN8svWgshbm6lB5jcIdgXr6usEa1SfO6rIAwKcRRoAmqHO5NXPpVj27bLsk6ezECM2+bjCzqgJACyCMAKew53CF7l6Yqy/zDkuSfnleVz145TkKCQqwuDIA8A+EEeAE3G6jV1fla8bizSqvcSncEagZ15yrHw9Isro0APArhBGgEfmHKnTfv9YrZ8chSdLQbtF68tqBSo3tYHFlAOB/CCPA99S63Ho5Z5f+8sEWVda6FBoUoHsv76MJ6akKsDNaBgBaA2EEOOqzbw/qkf/bpG8PlEmSzusRo8evGaBunegNAYDWRBhBu5dXVK4/vbdZH24ulCRFhwXp3sv7auywFNnpDQGAVkcYQbtV6KzSc8u269WVu1TrMgq02zQhPVV3XtqbKd0BoA0RRtDuHHBW6dll27VgVb5q6tySpIvP6qyHfnyOesUxbwgAtDXCCNqN3cUVmvvFTi1Yma/qoyFkWLdo3f2js3R+r1iLqwOA9oswAr9mjNHKncWa+/lOfbi5UG5T3z60W7TuzjhL5/fqxDVlAMBihBH4pdKKWv3f+n16dWW+Nu93etovOquzbrmwuy7oFUsIAQAvQRiB33C5jT7fVqR/rt6t/2wq9JwPEhJk1zVDumjS+anqFdfR4ioBAD9EGIFPq3W5tWpnsZZsKNAHGwt04Ei157k+8R3186FddO2wLooKC7awSgDAyRBG4HNKK2q1fHuRsr85oA83F6qkotbzXFRYkMYMStbPh3ZRv6QIDsUAgA8gjMDrVdW6tH5PqT7/9qA+21akr3aXeE5ElaSYDsG67Jx4ZfZP0MieneQI5Gq6AOBLCCPwKsYYFTqrlbu7RGt2FWv1rsPasLdUtS7TYLleceG6sHesMvslaFi3aAUG2C2qGABwpggjsExVrUs7DpZr+8Eybdrv1Ia9pdq0z6lD5TXHLdu5o0Np3WN00VmddWHvWCVGhlpQMQCgNZxWGJk9e7aeeOIJFRQUaODAgXr66ac1YsSIEy7/z3/+Uw899JDy8vLUu3dvPf7447riiitOu2j4joqaOu0rqdL+0krlFZVr+8Fy7Sgq1/YDZdpXWiljjl8nwG5T77hwDekWrWHdojWsW4xSYkI5/wMA/FSzw8jChQuVlZWlOXPmKC0tTbNmzVJmZqa2bNmiuLi445Zfvny5xo0bp+nTp+vHP/6xFixYoDFjxmjt2rXq379/i2wE2pbbbVRSWavi8moVl9ffHyqv0eHyGh04Uq19JZXaV1KlfaWVDU4ubUxUWJB6xHZQ38QI9UuKUL+kSPVN6KiQIM77AID2wmZMY3+bnlhaWpqGDx+uZ555RpLkdruVkpKiO+64Q/fff/9xy48dO1bl5eV69913PW3nnXeeBg0apDlz5jTpPZ1OpyIjI1VaWqqIiIjmlNvuudxG1XUuVde6VV3nrv+5zq2q2vr7+naXKmpcKquuU1lVXf19dZ3Kq+t05GhbeXXd0QBSo5KKmgYnkJ5KuCNQSVEh6hoTpp6dw9Wjcwf16Byunp3DFdOBIbcA4K+a+v3drJ6RmpoarVmzRlOmTPG02e12ZWRkKCcnp9F1cnJylJWV1aAtMzNTixYtOuH7VFdXq7r6u/kinE7nCZc9Ey9+vlO7iysk1Z84aSQZIxmZo/c6ehjBeA4nNPa8Uf0Dc9zrfPe4fuXvrXuS9zm2ntvUh4k6t/vovam/d5nG2z3Pu1XnNqqpq79vLREhgeoU7lB0WJBiOjjUqUOwYjsGKzEyVMlRoUqKClViVIgiQrgCLgDgxJoVRoqKiuRyuRQfH9+gPT4+Xt98802j6xQUFDS6fEFBwQnfZ/r06Xr44YebU9ppeXf9Pq3LL2n19/EWQQE2OQID5Ai0KySo/j440C5HUIBCg+wKdwSpY0igOjgCFO4IUrgjQOGOQHVwBKpjSKAiQoIUEx6smA7Big4LVhAjWAAALcArR9NMmTKlQW+K0+lUSkpKi7/Pz4d20cienWSTTTabZJMkm022+rsG7TabGpxA2ejzRx/XP//91/lu3Sa9j+ofBNhsCgywKcBuU6DdpgC7/ei97bv7gMbbA+wNg0dwoF0Bdk4ABQB4n2aFkdjYWAUEBKiwsLBBe2FhoRISEhpdJyEhoVnLS5LD4ZDD4WhOaadlfFq3Vn8PAABwcs3qZw8ODtbQoUOVnZ3taXO73crOzlZ6enqj66SnpzdYXpKWLl16wuUBAED70uzDNFlZWZo4caKGDRumESNGaNasWSovL9ekSZMkSRMmTFBycrKmT58uSbrzzjt18cUX68knn9SVV16p119/XatXr9bzzz/fslsCAAB8UrPDyNixY3Xw4EFNnTpVBQUFGjRokJYsWeI5STU/P192+3cdLiNHjtSCBQv04IMP6ve//7169+6tRYsWMccIAACQdBrzjFiBeUYAAPA9Tf3+ZmwmAACwFGEEAABYijACAAAsRRgBAACWIowAAABLEUYAAIClCCMAAMBShBEAAGApwggAALBUs6eDt8KxSWKdTqfFlQAAgKY69r19qsnefSKMHDlyRJKUkpJicSUAAKC5jhw5osjIyBM+7xPXpnG73dq3b586duwom83WYq/rdDqVkpKi3bt3++01b9hG3+fv2yexjf7A37dP8v9tbI3tM8boyJEjSkpKanAR3R/yiZ4Ru92uLl26tNrrR0RE+OV/rO9jG32fv2+fxDb6A3/fPsn/t7Glt+9kPSLHcAIrAACwFGEEAABYql2HEYfDoWnTpsnhcFhdSqthG32fv2+fxDb6A3/fPsn/t9HK7fOJE1gBAID/atc9IwAAwHqEEQAAYCnCCAAAsBRhBAAAWKrdhJFHH31UI0eOVFhYmKKiohpdJj8/X1deeaXCwsIUFxen3/3ud6qrq2uwzLJlyzRkyBA5HA716tVL8+fPb/3iT8OyZctks9kavX355ZeSpLy8vEafX7FihcXVN01qaupxtc+YMaPBMuvXr9eFF16okJAQpaSk6M9//rNF1TZfXl6ebrrpJnXv3l2hoaHq2bOnpk2bppqamgbL+PI+lKTZs2crNTVVISEhSktL06pVq6wu6bRNnz5dw4cPV8eOHRUXF6cxY8Zoy5YtDZa55JJLjttft912m0UVN88f/vCH42rv27ev5/mqqipNnjxZnTp1Unh4uK655hoVFhZaWHHzNfa5YrPZNHnyZEm+uf8+/fRTXXXVVUpKSpLNZtOiRYsaPG+M0dSpU5WYmKjQ0FBlZGTo22+/bbBMcXGxxo8fr4iICEVFRemmm25SWVlZyxVp2ompU6eamTNnmqysLBMZGXnc83V1daZ///4mIyPDrFu3zixevNjExsaaKVOmeJbZsWOHCQsLM1lZWWbTpk3m6aefNgEBAWbJkiVtuCVNU11dbfbv39/gdvPNN5vu3bsbt9ttjDFm586dRpL58MMPGyxXU1NjcfVN061bN/PII480qL2srMzzfGlpqYmPjzfjx483GzZsMK+99poJDQ01f/vb3yysuunef/99c8MNN5gPPvjAbN++3bzzzjsmLi7O3HPPPZ5lfH0fvv766yY4ONjMnTvXbNy40dxyyy0mKirKFBYWWl3aacnMzDTz5s0zGzZsMLm5ueaKK64wXbt2bfD/8uKLLza33HJLg/1VWlpqYdVNN23aNNOvX78GtR88eNDz/G233WZSUlJMdna2Wb16tTnvvPPMyJEjLay4+Q4cONBg+5YuXWokmY8//tgY45v7b/HixeaBBx4wb731lpFk3n777QbPz5gxw0RGRppFixaZr776ylx99dWme/fuprKy0rPM5ZdfbgYOHGhWrFhhPvvsM9OrVy8zbty4Fqux3YSRY+bNm9doGFm8eLGx2+2moKDA0/bcc8+ZiIgIU11dbYwx5t577zX9+vVrsN7YsWNNZmZmq9bcEmpqakznzp3NI4884mk79kW2bt066wo7A926dTNPPfXUCZ9/9tlnTXR0tGf/GWPMfffdZ/r06dMG1bWOP//5z6Z79+6ex76+D0eMGGEmT57seexyuUxSUpKZPn26hVW1nAMHDhhJ5pNPPvG0XXzxxebOO++0rqgzMG3aNDNw4MBGnyspKTFBQUHmn//8p6dt8+bNRpLJyclpowpb3p133ml69uzp+SPOl/efMea4MOJ2u01CQoJ54oknPG0lJSXG4XCY1157zRhjzKZNm4wk8+WXX3qWef/9943NZjN79+5tkbrazWGaU8nJydG5556r+Ph4T1tmZqacTqc2btzoWSYjI6PBepmZmcrJyWnTWk/Hv//9bx06dEiTJk067rmrr75acXFxuuCCC/Tvf//bgupO34wZM9SpUycNHjxYTzzxRIPDajk5ObrooosUHBzsacvMzNSWLVt0+PBhK8o9Y6WlpYqJiTmu3Rf3YU1NjdasWdPgd8putysjI8MnfqeaorS0VJKO22evvvqqYmNj1b9/f02ZMkUVFRVWlHdavv32WyUlJalHjx4aP3688vPzJUlr1qxRbW1tg/3Zt29fde3a1Wf3Z01Njf7xj3/oxhtvbHCRVl/efz+0c+dOFRQUNNhvkZGRSktL8+y3nJwcRUVFadiwYZ5lMjIyZLfbtXLlyhapwyculNcWCgoKGgQRSZ7HBQUFJ13G6XSqsrJSoaGhbVPsaXjxxReVmZnZ4IKD4eHhevLJJ3X++efLbrfrX//6l8aMGaNFixbp6quvtrDapvmf//kfDRkyRDExMVq+fLmmTJmi/fv3a+bMmZLq91f37t0brPP9fRodHd3mNZ+Jbdu26emnn9Zf/vIXT5sv78OioiK5XK5Gf6e++eYbi6pqOW63W3fddZfOP/989e/f39N+3XXXqVu3bkpKStL69et13333acuWLXrrrbcsrLZp0tLSNH/+fPXp00f79+/Xww8/rAsvvFAbNmxQQUGBgoODjzsnLz4+3vMZ6msWLVqkkpIS3XDDDZ42X95/jTm2bxr7Pfz+d19cXFyD5wMDAxUTE9Ni+9anw8j999+vxx9//KTLbN68ucEJVr7udLZ5z549+uCDD/TGG280WC42NlZZWVmex8OHD9e+ffv0xBNPWPZF1pzt+37tAwYMUHBwsH71q19p+vTpXj1d8+nsw7179+ryyy/Xtddeq1tuucXT7o37EPUmT56sDRs26PPPP2/Qfuutt3p+Pvfcc5WYmKhLL71U27dvV8+ePdu6zGYZPXq05+cBAwYoLS1N3bp10xtvvOHVf4ydrhdffFGjR49WUlKSp82X95838+kwcs899zRIrI3p0aNHk14rISHhuLP4j50FnpCQ4Ln/4ZnhhYWFioiIaLNfxNPZ5nnz5qlTp05N+nJKS0vT0qVLz6TEM3Im+zQtLU11dXXKy8tTnz59Tri/pO/2qRWau4379u3TqFGjNHLkSD3//POnfH2r92FTxcbGKiAgoNF9ZOX+aQm/+c1v9O677+rTTz9t0BvZmLS0NEn1PV++9mUWFRWls846S9u2bdOPfvQj1dTUqKSkpEHviK/uz127dunDDz88ZY+HL+8/6bvPwsLCQiUmJnraCwsLNWjQIM8yBw4caLBeXV2diouLW2zf+nQY6dy5szp37twir5Wenq5HH31UBw4c8HRHLV26VBERETrnnHM8yyxevLjBekuXLlV6enqL1NAUzd1mY4zmzZunCRMmKCgo6JTL5+bmNvgP2dbOZJ/m5ubKbrd79l96eroeeOAB1dbWerZ96dKl6tOnj6WHaJqzjXv37tWoUaM0dOhQzZs3T3b7qU/zsnofNlVwcLCGDh2q7OxsjRkzRlL9oY3s7Gz95je/sba402SM0R133KG3335by5YtO+4wYWNyc3MlySf22Q+VlZVp+/btuv766zV06FAFBQUpOztb11xzjSRpy5Ytys/Pb9PPyJYyb948xcXF6corrzzpcr68/ySpe/fuSkhIUHZ2tid8OJ1OrVy5Urfffruk+s/SkpISrVmzRkOHDpUkffTRR3K73Z4wdsZa5DRYH7Br1y6zbt068/DDD5vw8HCzbt06s27dOnPkyBFjzHdDey+77DKTm5trlixZYjp37tzo0N7f/e53ZvPmzWb27NleO7T3mA8//NBIMps3bz7uufnz55sFCxaYzZs3m82bN5tHH33U2O12M3fuXAsqbZ7ly5ebp556yuTm5prt27ebf/zjH6Zz585mwoQJnmVKSkpMfHy8uf76682GDRvM66+/bsLCwnxmaO+ePXtMr169zKWXXmr27NnTYCjhMb68D42pH9rrcDjM/PnzzaZNm8ytt95qoqKiGoxq8yW33367iYyMNMuWLWuwvyoqKowxxmzbts088sgjZvXq1Wbnzp3mnXfeMT169DAXXXSRxZU3zT333GOWLVtmdu7cab744guTkZFhYmNjzYEDB4wx9UN7u3btaj766COzevVqk56ebtLT0y2uuvlcLpfp2rWrue+++xq0++r+O3LkiOc7T5KZOXOmWbdundm1a5cxpn5ob1RUlHnnnXfM+vXrzU9+8pNGh/YOHjzYrFy50nz++eemd+/eDO09HRMnTjSSjrsdGztujDF5eXlm9OjRJjQ01MTGxpp77rnH1NbWNnidjz/+2AwaNMgEBwebHj16mHnz5rXthjTTuHHjTjjOf/78+ebss882YWFhJiIiwowYMaLBsDxvtmbNGpOWlmYiIyNNSEiIOfvss81jjz1mqqqqGiz31VdfmQsuuMA4HA6TnJxsZsyYYVHFzTdv3rxG/89+/28IX96Hxzz99NOma9euJjg42IwYMcKsWLHC6pJO24n217HPifz8fHPRRReZmJgY43A4TK9evczvfvc7r5+n4pixY8eaxMREExwcbJKTk83YsWPNtm3bPM9XVlaaX//61yY6OtqEhYWZn/70pw3Cs6/44IMPjCSzZcuWBu2+uv8+/vjjRv9fTpw40RhTP7z3oYceMvHx8cbhcJhLL730uG0/dOiQGTdunAkPDzcRERFm0qRJnj/mW4LNGGNapo8FAACg+ZhnBAAAWIowAgAALEUYAQAAliKMAAAASxFGAACApQgjAADAUoQRAABgKcIIAACwFGEEAABYijACAAAsRRgBAACWIowAAABL/T/7mxVQCrd9+wAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(-100, 100, 1.0)\n",
    "y = sps.norm.cdf(x, loc = 0, scale = 25)\n",
    "\n",
    "F = plt.figure()\n",
    "A = F.add_subplot()\n",
    "\n",
    "A.plot(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "406342be",
   "metadata": {},
   "source": [
    "<strong>График функции равномерного распределения</strong>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "213ba051",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(-20, 100, 1.0)\n",
    "y = sps.uniform.cdf(x, loc = 0, scale = 80)\n",
    "\n",
    "F = plt.figure()\n",
    "A = F.add_subplot()\n",
    "\n",
    "A.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8763bae",
   "metadata": {},
   "source": [
    "<strong>График функции экспоненциального распределения</strong>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "76a7205c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(-20, 100, 1.0)\n",
    "y = sps.expon.cdf(x, loc = 0, scale = 20)\n",
    "\n",
    "F = plt.figure()\n",
    "A = F.add_subplot()\n",
    "\n",
    "A.plot(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14727a7a",
   "metadata": {},
   "source": [
    "<strong>График функции распределения Коши</strong>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8d2cfd60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(-100, 100, 1.0)\n",
    "y = sps.cauchy.cdf(x, loc = 0, scale = 20)\n",
    "\n",
    "F = plt.figure()\n",
    "A = F.add_subplot()\n",
    "\n",
    "A.plot(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9a9cb8d",
   "metadata": {},
   "source": [
    "<strong>График функции распределения Пуассона</strong>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d1630fa0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(0, 20, 0.2)\n",
    "y = sps.poisson.cdf(x, mu = 10)\n",
    "\n",
    "F = plt.figure()\n",
    "A = F.add_subplot()\n",
    "\n",
    "A.plot(x, y)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
